Mathematical optimization algorithms with applications to data analytics; primary topics include convexity, gradient and subgradient methods and their variants, Newton’s and quasi-Newton methods, Frank-Wolfe, duality, derivative-free optimization. Prerequisites: Grade of C or better in MATH 251 or MATH 253; grade of C or better in MATH 304 or MATH 323 Credits 3. 3 Lecture Hours.
Mathematical optimization algorithms with applications to data analytics; primary topics include convexity, gradient and subgradient methods and their variants, Newton’s and quasi-Newton methods, Frank-Wolfe, duality, derivative-free optimization. Prerequisites: Grade of C or better in MATH 251 or MATH 253; grade of C or better in MATH 304 or MATH 323 Credits 3. 3 Lecture Hours.
